Apparatus and method for predicting an amount of network infrastructure needed based on demographics

ABSTRACT

Methods, computer-readable media and apparatuses for predicting an amount of network infrastructure needed for a new neighborhood are disclosed. A processor generates a plurality of different user profiles based upon demographic data of existing customers, historical utilization data and historical usage data, determines a demographic of a new neighborhood, correlates one of the plurality of different user profiles to the new neighborhood based upon the demographic of the new neighborhood and predicts the amount of network infrastructure to be deployed in the new neighborhood based upon the one of the plurality of different user profiles that is correlated to the demographic of the new neighborhood.

The present disclosure relates generally to deploying networkinfrastructure and, more particularly, to an apparatus, method and acomputer-readable medium for predicting an amount of networkinfrastructure needed based on demographics.

BACKGROUND

Previously, network infrastructure investment and deployment were basedhistorical data that was backwards looking. For example, past usage wasused to extrapolate usage in the future regardless of the users orindividuals. However, the types of services used by individuals and thetypes of individuals within a neighborhood can evolve over time. Thus,attempting to determine an amount of network infrastructure needed for anew neighborhood using an average that is based on generic historicaldata may be inaccurate in predicting the amount of networkinfrastructure that may be needed in the new neighborhood.

SUMMARY

In one example, the present disclosure discloses a method,computer-readable medium, and apparatus for predicting an amount ofnetwork infrastructure needed for a new neighborhood. For example, themethod may include a processor that generates a plurality of differentuser profiles based upon demographic data of existing customers,historical utilization data and historical usage data, determines ademographic of a new neighborhood, correlates one of the plurality ofdifferent user profiles to the new neighborhood based upon thedemographic of the new neighborhood and predicts the amount of networkinfrastructure to be deployed in the new neighborhood based upon the oneof the plurality of different user profiles that is correlated to thedemographic of the new neighborhood.

BRIEF DESCRIPTION OF THE DRAWINGS

The teaching of the present disclosure can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example network related to the present disclosure;

FIG. 2 illustrates an example block diagram of the present disclosure;

FIG. 3 illustrates a flowchart of an example method for predicting anamount of network infrastructure needed for a new neighborhood based ondemographics; and

FIG. 4 illustrates a high-level block diagram of a computer suitable foruse in performing the functions described herein.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION

The present disclosure broadly discloses methods, computer-readablemedia and devices for predicting an amount of network infrastructureneeded for a new neighborhood based on demographics. As discussed above,network infrastructure investment and deployment were previously basedon historical data that was backwards looking. For example, past usagewas used to extrapolate usage in the future regardless of the users orindividuals. However, the types of services used by individuals and thetypes of individuals within a neighborhood can evolve over time. Thus,attempting to determine an amount of network infrastructure needed for anew neighborhood using an average that is based on generic historicaldata may be inaccurate in predicting the amount of networkinfrastructure that may be needed in the new neighborhood.

One embodiment of the present disclosure allows a network serviceprovider to predict an amount of network infrastructure needed based ondemographics of a new neighborhood. For example, usage patterns ofexisting customers may be collected and analyzed to generate a pluralityof different user profiles for different demographic groups. Each one ofthe user profiles may be transformed into data regarding an amount ofone or more different network elements that may be needed for a networkinfrastructure that may be deployed in a new neighborhood that has asimilar demographic as one of the user profiles.

In addition, the value of each user associated with the identified userprofile may be calculated and compared to the cost of deploying thenecessary amounts of network elements in the network infrastructure forthe new neighborhood. If the value is greater than the costs, arecommendation may be made to proceed with deployment of the necessarynetwork elements for the network infrastructure.

To aid in understanding the present disclosure, FIG. 1 illustrates acommunication network 100. In one embodiment, the communication network100 may include an Internet Protocol (IP) network 102. The IP network102 may include an application server (AS) 104 and a database (DB) 106.Although only a single AS 104 and a single DB 106 is illustrated in FIG.1, it should be noted that any number of application servers anddatabases may be deployed.

In one embodiment, the AS 104 may be deployed as a computer having aprocessor and a memory as illustrated in FIG. 4 and discussed below. Inone embodiment, the AS 104 may be configured to perform the functionsdescribed below.

In one embodiment, the DB 106 may store a variety of user data that isused to generate user profiles. The user profiles may then also bestored in the DB 106. In one embodiment, the user data may include auser demographic data (e.g., user age, gender, income, marital status,profession, location, race, and the like), service usage history (e.g.,which services of the service provider that the user is subscribed to,such as, video, Internet, landline telephone, wireless telephone, homesecurity services, and the like), service utilization history (e.g., howmuch of each one of the services that the user uses) and householddemographic data (e.g., who are the members in the user's household andeach one of the members' demographic data).

In one embodiment, the DB 106 may also store information regarding costto deploy each one of a plurality of different network elements neededfor a network infrastructure. For example, the costs to deploy a networkelement may include a cost of the network element, a cost to install thenetwork element, a cost to maintain the network element, a cost tooperate the network element, and the like.

It should be noted that the IP network 102 has been simplified for easeof explanation. The IP network 102 may include additional networkelements that are not shown, such as for example, a gateway (e.g., aserving gateway, a packet data network gateway, and the like), afirewall, a call control element, one or more access networks, anauthentication, authorization and accounting server, a home subscriberserver, a mobility management entity, and the like. In one embodiment,the IP network 102 may be a cellular communication network (e.g., a 3G,4G, LTE, and the like).

In one embodiment, the communication network 100 may also includeexisting neighborhoods 108 and 110. In one embodiment, the existingneighborhoods 108 and 110 may be in different geographic locations. Inone embodiment, a “neighborhood” may be defined as a single building(e.g., a commercial building or apartment building), a single housingdevelopment, a single school district, a single town or city, a singlestate, and the like. Although two existing neighborhoods 108 and 110 areillustrated in FIG. 1 by example, it should be noted that any number ofneighborhoods may be deployed.

In one embodiment, the existing neighborhoods 108 and 110 may have acorresponding network infrastructure (NI) 120 and 122, respectively. Thenetwork infrastructure 120 and 122 may include one or more of aplurality of different network elements (e.g., border elements,gateways, cellular towers, fiber lines, firewalls, routers, switches,and the like).

In one embodiment, the existing neighborhood 108 may include one or moreusers 112 ₁ to 112 _(n) (herein referred to collectively as users 112 orindividually as a user 112) and the existing neighborhood 110 mayinclude one or more users 114 ₁ to 114 _(n) (herein referred tocollectively as users 114 or individually as a user 114). In oneembodiment, the AS 104 may collect information about the users 112 and114 to generate one or more user profiles. For example, the AS 104 maycollect user demographic data (e.g., user age, gender, income, maritalstatus, profession, location, race, and the like), service usage history(e.g., which services of the service provider the user is subscribed to,such as, video, internet, landline telephone, wireless telephone, homesecurity services, and the like), service utilization history (e.g., howmuch of each one of the services the user uses) and householddemographic data (e.g., who are the members in the user's household andeach one of the members demographic data).

Based on the information that is collected, one or more user profilesmay be generated for the existing neighborhoods 108 and 110. In oneexample, the user profiles may be based upon an average of the datacollected for the users 112 and 114 within the existing neighborhoods108 and 110, respectively. In another example, the user profiles may bebased upon a normal Gaussian distribution of the data. For example, auser profile 1 of the existing neighborhood 108 may be determined to bemostly married families who are between the ages of 30-40 with twochildren under the age of 12. Most of the families are serviceprofessionals with an average household income of $100,000 a year. Userprofile 1 predicts that the user will only subscribe to cable, Internetand wireless telephone services. In addition, a particular type andamount of network elements will be needed to support X amount of cable,Y amount data for Internet services and Z amount of wireless telephoneservices for user profile 1.

In another example, a user profile 2 of the existing neighborhood 110may be determined to be mostly single males who are between the ages of20-30. The average user is an hourly wage worker with a household incomeof $50,000 a year. This particular user profile predicts that the userwill only subscribe to Internet and wireless telephone services. Userprofile 2 predicts that the user will only subscribe to Internet andwireless telephone services. In addition, a particular type and amountof network elements will be needed to support A amount data for Internetservices and B amount of wireless telephone services for user profile 2.

In one embodiment, the user profiles 1 and 2 may be used to predict anamount of network infrastructure 124 needed for a new neighborhood 116having users 118 ₁ to 118 _(n). The term “new neighborhood” may bedefined as being “new” to the service provider. For example, the newneighborhood 116 may have existed, but without any networkinfrastructure 124 from the service provider. In another example, theterm “new neighborhood” may be defined as being a newly constructedbuilding, housing development, town, city and the like.

In one embodiment, user demographic data and household demographic datamay be collected for the users 118 of the new neighborhood 116. Thedemographic data may then be compared to the user profiles (e.g., userprofiles 1 and 2) to determine, which user profile most closely matchesthe demographic data of the new neighborhood 116. Based on the userprofile that most closely matches the demographic data of the newneighborhood 116, a prediction may be made on an amount of networkinfrastructure that is needed for the new neighborhood 116. Arecommendation may be made on whether the new network infrastructure 124should be deployed or not based on estimated revenue associated with thenetwork usage of the user profile that matches the demographic data ofthe new neighborhood 116.

FIG. 2 illustrates an example block diagram of how the amount of networkinfrastructure is predicted. As discussed above, a database 202 maystore user demographics, a database 204 may store service utilizationhistory, a database 206 may store service usage history and a database208 may store household demographics. The collected information may thenbe used to derive a user profile at block 212.

In one embodiment, at block 216, a new neighborhood utilization forecastmay be calculated. As discussed above, the demographic data of the newneighborhood may be collected and compared to the generated userprofiles from block 212. The user profile may provide a predictionregarding which services that the user will subscribe to and how much ofeach service the user will use. The user profile that most closelymatches the demographic data of the new neighborhood may be used tocalculate the new neighborhood utilization forecast.

In one embodiment, the new neighborhood utilization forecast may be usedto compute an amount of network infrastructure that is needed for thenew neighborhood at block 218. In one embodiment, a database 222 thatstores information regarding existing network infrastructures may beused to compute the amount of infrastructure that is needed in the newneighborhood.

A database 220 may store a cost associated with each one of a pluralityof different network elements that may be deployed in the networkinfrastructure. The information may be used with the amount of networkinfrastructure that is needed for the new neighborhood to calculate areport on expected area usage at block 224. The report on expected areausage at block 224 may include a cost associated with deploying theamount of network infrastructure for the new neighborhood.

In one embodiment, a database 210 may be used to store informationrelated to service subscriptions. For example, the database 210 may beused to obtain information related to how much revenue each servicesubscription generates. At block 214, the service subscriptioninformation may be used with the user profiles generated at block 212 tocompute a subscription value per user profile. In other words, basedupon the predicted services that are subscribed to and an amount eachservice is used according to the user profiles, each user profile may beassociated with a subscription value or an amount of revenue that isgenerated.

In one embodiment, a database 226 may be used to identify households byneighborhoods. At block 228, the information from the database 226 maybe used to identify un-served neighborhoods and potential customers(e.g., new neighborhoods). In one embodiment, the subscription value peruser profile from block 214 may then be used to compute a potentialvalue per un-served neighborhoods/potential customers.

In one embodiment, the subscription value of un-servedneighborhoods/potential customers calculated in block 230 may becombined with the report on expected area usage in block 224 to generatea report on new neighborhood investment net value in block 232. Forexample, the report on new neighborhood investment net value may besubscription value of un-served neighborhoods/potential customerscalculated in block 230 less the cost associated with the amount ofnetwork infrastructure required for the expected area usage calculatedin block 224.

In one embodiment, if the report on new neighborhood investment netvalue is positive, the AS 104 may generate a recommendation that theinvestment to deploy the amount of network infrastructure in the newneighborhood 116 should proceed. However, if the report on newneighborhood investment net value is negative, the AS 104 may generate arecommendation that the investment to deploy the amount of networkinfrastructure in the new neighborhood 116 should not proceed.

FIG. 3 illustrates a flowchart of a method 300 for predicting an amountof network infrastructure needed for a new neighborhood based ondemographics in accordance with the present disclosure. In oneembodiment, steps, functions and/or operations of the method 300 may beperformed by an AS 104. In one embodiment, the steps, functions, oroperations of method 300 may be performed by a computing device orsystem 400, and/or processor 402 as described in connection with FIG. 4below. For illustrative purpose, the method 300 is described in greaterdetail below in connection with an embodiment performed by a processor,such as processor 402.

The method 300 begins in step 302. At step 304, a processor generates aplurality of user profiles. For example, information related to theusers within an existing neighborhood may be collected to generate theplurality of user profiles. In one example, the information may includeuser demographic data (e.g., user age, gender, income, marital status,profession, location, race, and the like), service usage history (e.g.,which services of the service provider the user is subscribed to, suchas, video, internet, landline telephone, wireless telephone, homesecurity services, and the like), service utilization history (e.g., howmuch of each one of the services the user uses) and householddemographic data (e.g., who are the members in the user's household andeach one of the members demographic data).

At step 306, the processor determines a demographic of a newneighborhood. For example, the new neighborhood may be a neighborhoodthat exists, but has not services from a service provider. In otherwords, the service provider may want to deploy a network infrastructurein the “new neighborhood” to obtain potentially new customers. Thedemographic of the new neighborhood may be obtained from census data,tax records, and the like.

At step 308, the processor correlates one of the plurality of differentuser profiles to the new neighborhood based upon the demographic of thenew neighborhood. For example, the user profile that most closelymatches the demographic information of the new neighborhood may be usedto predict the amount of network infrastructure needed for the newneighborhood.

At step 310, the processor predicts the amount of network infrastructureto be deployed in the new neighborhood based upon the one of theplurality of different user profiles that is correlated to thedemographic of the new neighborhood. For example, each one of theplurality of different user profiles may provide predictions regardingwhat type of services the user will most likely subscribe to and howmuch of each service the user will most likely use. Based on the userprofile that is correlated, the amount of network infrastructure to bedeployed in the new neighborhood may be predicted.

At step 312, the processor calculates a value for the one of theplurality of different user profiles. For example, based upon theservices and the amount of services that are predicted by the userprofile, an amount of revenue generated for the user profile may becalculated.

At step 314, the processor calculates a cost to deploy the amount of oneor more network elements for the network infrastructure. For example,the amount of network infrastructure that should be deployed may includean amount of one or more network elements. The amount of each one of thedifferent network elements may be calculated based upon the amount ofnetwork infrastructure that is needed. In addition, a cost associatedwith each one of the different network elements that are needed may becalculated (e.g., cost of equipment, cost of installation, cost ofmaintenance, cost of operation, and the like).

At step 316, the processor determines if the value is greater than thecost. If the value is greater than the cost, then the method 300 mayproceed to step 318.

At step 318, the processor may generate a recommendation to proceed withthe deployment. The method then proceeds to step 322.

Referring back to step 316, if the processor determines that the valueis not greater than the cost, the method 300 proceeds to step 320. Atstep 320, the processor generates a recommendation to not proceed withthe deployment. The method 300 then proceeds to step 322. At step 322,the method 300 ends.

It should be noted that although not specifically specified, one or moresteps, functions or operations of the method 300 may include a storing,displaying and/or outputting step as required for a particularapplication. In other words, any data, records, fields, and/orintermediate results discussed in the respective methods can be stored,displayed and/or outputted to another device as required for aparticular application. Furthermore, steps or blocks in FIG. 3 thatrecite a determining operation or involve a decision do not necessarilyrequire that both branches of the determining operation be practiced. Inother words, one of the branches of the determining operation can bedeemed as an optional step. In addition, one or more steps, blocks,functions or operations of the above described method 300 may compriseoptional steps, or can be combined, separated, and/or performed in adifferent order from that described above, without departing from theexample embodiments of the present disclosure.

As such, the present disclosure provides at least one advancement in thetechnical field of communication networks. This advancement allows forpredicting an amount of network infrastructure needed for a newneighborhood based on demographics. The present disclosure also providesa transformation of data. For example, user profiles generated based ondemographic data, historical utilization data and historical usage datais transformed into a prediction of an amount of network infrastructureto be deployed in a new neighborhood.

Finally, embodiments of the present disclosure improve the functioningof a computing device, e.g., a server, a base station, an eNodeB and/ora UE. For example, an application server dedicated for predicting anamount of network infrastructure needed for a new neighborhood based ondemographics that was not previously available.

FIG. 4 depicts a high-level block diagram of a computing device suitablefor use in performing the functions described herein. As depicted inFIG. 4, the system 400 comprises one or more hardware processor elements402 (e.g., a central processing unit (CPU), a microprocessor, or amulti-core processor), a memory 404 (e.g., random access memory (RAM)and/or read only memory (ROM)), a module 405 for predicting an amount ofnetwork infrastructure needed for a new neighborhood based ondemographics, and various input/output devices 406 (e.g., storagedevices, including but not limited to, a tape drive, a floppy drive, ahard disk drive or a compact disk drive, a receiver, a transmitter, aspeaker, a display, a speech synthesizer, an output port, an input portand a user input device (such as a keyboard, a keypad, a mouse, amicrophone and the like)). Although only one processor element is shown,it should be noted that the computing device may employ a plurality ofprocessor elements. Furthermore, although only one computing device isshown in the figure, if the method 300, as discussed above, isimplemented in a distributed or parallel manner for a particularillustrative example, i.e., the steps of the above method 300, or theentirety of method 300 is implemented across multiple or parallelcomputing device, then the computing device of this figure is intendedto represent each of those multiple computing devices.

Furthermore, one or more hardware processors can be utilized insupporting a virtualized or shared computing environment. Thevirtualized computing environment may support one or more virtualmachines representing computers, servers, or other computing devices. Insuch virtualized virtual machines, hardware components such as hardwareprocessors and computer-readable storage devices may be virtualized orlogically represented.

It should be noted that the present disclosure can be implemented insoftware and/or in a combination of software and hardware, e.g., usingapplication specific integrated circuits (ASIC), a programmable gatearray (PGA) including a Field PGA, or a state machine deployed on ahardware device, a computing device or any other hardware equivalents,e.g., computer readable instructions pertaining to the method discussedabove can be used to configure a hardware processor to perform thesteps, functions and/or operations of the above disclosed method 300. Inone embodiment, instructions and data for the present module or process405 for predicting an amount of network infrastructure needed for a newneighborhood based on demographics (e.g., a software program comprisingcomputer-executable instructions) can be loaded into memory 404 andexecuted by hardware processor element 402 to implement the steps,functions or operations as discussed above in connection with theillustrative method 300. Furthermore, when a hardware processor executesinstructions to perform “operations”, this could include the hardwareprocessor performing the operations directly and/or facilitating,directing, or cooperating with another hardware device or component(e.g., a co-processor and the like) to perform the operations.

The processor executing the computer readable or software instructionsrelating to the above described method can be perceived as a programmedprocessor or a specialized processor. As such, the present module 405for predicting an amount of network infrastructure needed for a newneighborhood based on demographics (including associated datastructures) of the present disclosure can be stored on a tangible orphysical (broadly non-transitory) computer-readable storage device ormedium, e.g., volatile memory, non-volatile memory, ROM memory, RAMmemory, magnetic or optical drive, device or diskette and the like.Furthermore, a “tangible” computer-readable storage device or mediumcomprises a physical device, a hardware device, or a device that isdiscernible by the touch. More specifically, the computer-readablestorage device may comprise any physical devices that provide theability to store information such as data and/or instructions to beaccessed by a processor or a computing device such as a computer or anapplication server.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and nota limitation. Thus, the breadth and scope of a preferred embodimentshould not be limited by any of the above-described exemplaryembodiments, but should be defined only in accordance with the followingclaims and their equivalents.

What is claimed is:
 1. A method for predicting an amount of networkinfrastructure needed for a new neighborhood, the method comprising:generating, by a processor, a plurality of different user profiles basedupon demographic data of existing customers, historical utilization dataand historical usage data, wherein the demographic data comprises userdemographic data and household demographic data; determining, by theprocessor, a demographic of the new neighborhood; correlating, by theprocessor, one of the plurality of different user profiles to the newneighborhood based upon the demographic of the new neighborhood; andpredicting, by the processor, the amount of network infrastructure to bedeployed in the new neighborhood based upon the one of the plurality ofdifferent user profiles that is correlated to the demographic of the newneighborhood.
 2. The method of claim 1, wherein the amount of networkinfrastructure comprises an amount of each one of one or more differentnetwork elements.
 3. The method of claim 2, further comprising:calculating, by the processor, a value for the one of the plurality ofdifferent user profiles; calculating, by the processor, a cost to deploythe amount of the each one of the one or more different types of networkelements for the network infrastructure; and recommending, by theprocessor, to proceed with a deployment of the network infrastructure inthe new neighborhood when the value is greater than the cost.
 4. Themethod of claim 1, wherein the user demographic data comprises at leastone of: an age, a household income, an occupation, a gender, a race or ageographic location.
 5. The method of claim 1, wherein the householddemographic data comprises how many people are in a household anddemographic data for each person in the household.
 6. The method ofclaim 1, wherein the predicting further comprises: determining, by theprocessor, which types of services are used and an amount of each one ofthe services that is used by the one of the plurality of differentprofiles correlated to the new neighborhood; determining, by theprocessor, one or more different types of network elements required tosupport the types of services and the amount of the each one of theservices that is used; and calculating, by the processor, an amount ofeach one of the one or more different types of network elements tosupport the types of services and the amount of the each one of theservices that is used.
 7. A non-transitory computer-readable mediumstoring instructions which, when executed by a processor, cause theprocessor to perform operations for predicting an amount of networkinfrastructure needed for a new neighborhood, the operations comprising:generating a plurality of different user profiles based upon demographicdata of existing customers, historical utilization data and historicalusage data, wherein the demographic data comprises user demographic dataand household demographic data; determining a demographic of the newneighborhood; correlating one of the plurality of different userprofiles to the new neighborhood based upon the demographic of the newneighborhood; and predicting the amount of network infrastructure to bedeployed in the new neighborhood based upon the one of the plurality ofdifferent user profiles that is correlated to the demographic of the newneighborhood.
 8. The non-transitory computer-readable medium of claim 7,wherein the amount of network infrastructure comprises an amount of eachone of one or more different network elements.
 9. The non-transitorycomputer-readable medium of claim 8, wherein the operations furthercomprise: calculating a value for the one of the plurality of differentuser profiles; calculating a cost to deploy the amount of the each oneof the one or more different types of network elements for the networkinfrastructure; and recommending to proceed with a deployment of thenetwork infrastructure in the new neighborhood when the value is greaterthan the cost.
 10. The non-transitory computer-readable medium of claim7, wherein the user demographic data comprises at least one of: an age,a household income, an occupation, a gender, a race or a geographiclocation.
 11. The non-transitory computer-readable medium of claim 7,wherein the household demographic data comprises how many people are ina household and demographic data for each person in the household. 12.The non-transitory computer-readable medium of claim 7, wherein thepredicting further comprises: determining which types of services areused and an amount of each one of the services that is used by the oneof the plurality of different profiles correlated to the newneighborhood; determining one or more different types of networkelements required to support the types of services and the amount of theeach one of the services that is used; and calculating an amount of eachone of the one or more different types of network elements to supportthe types of services and the amount of the each one of the servicesthat is used.
 13. An apparatus for predicting an amount of networkinfrastructure needed for a new neighborhood, the apparatus comprising:a processor; and a computer-readable storage device storing a pluralityof instructions which, when executed by the processor, cause theprocessor to perform operations, the operations comprising: generating aplurality of different user profiles based upon demographic data ofexisting customers, historical utilization data and historical usagedata, wherein the demographic data comprises user demographic data andhousehold demographic data; determining a demographic of the newneighborhood; correlating one of the plurality of different userprofiles to the new neighborhood based upon the demographic of the newneighborhood; and predicting the amount of network infrastructure to bedeployed in the new neighborhood based upon the one of the plurality ofdifferent user profiles that is correlated to the demographic of the newneighborhood.
 14. The apparatus of claim 13, wherein the amount ofnetwork infrastructure comprises an amount of each one of one or moredifferent network elements.
 15. The apparatus of claim 14, wherein theoperations further comprise: calculating a value for the one of theplurality of different user profiles; calculating a cost to deploy theamount of the each one of the one or more different types of networkelements for the network infrastructure; and recommending to proceedwith a deployment of the network infrastructure in the new neighborhoodwhen the value is greater than the cost.
 16. The apparatus of claim 13,wherein the user demographic data comprises at least one of: an age, ahousehold income, an occupation, a gender, a race or a geographiclocation.
 17. The apparatus of claim 13, wherein the predicting furthercomprises: determining which types of services are used and an amount ofeach one of the services that is used by the one of the plurality ofdifferent profiles correlated to the new neighborhood; determining oneor more different types of network elements required to support thetypes of services and the amount of the each one of the services that isused; and calculating an amount of each one of the one or more differenttypes of network elements to support the types of services and theamount of the each one of the services that is used.
 18. The apparatusof claim 13, wherein the household demographic data comprises how manypeople are in a household and demographic data for each person in thehousehold.